Abstract
A typical navigation system for a Micro Aerial Vehicle (MAV) relies basically on GPS for position estimation. However, for several kinds of applications, the precision of the GPS is inappropriate or even its signal can be unavailable. In this context, and due to its flexibility, Monocular Simultaneous Localization and Mapping (SLAM) methods have become a good alternative for implementing visual-based navigation systems for MAVs that must operate in GPS-denied environments. On the other hand, one of the most important challenges that arises with the use of the monocular vision is the difficulty to recover the metric scale of the world. In this work, a monocular SLAM system for MAVs is presented. In order to overcome the problem of the metric scale, a novel technique for inferring the approximate depth of visual features from an ultrasonic range-finder is developed. Additionally, the altitude of the vehicle is updated using the pressure measurements of a barometer. The proposed approach is supported by the theoretical results obtained from a nonlinear observability test. Experiments performed with both computer simulations and real data are presented in order to validate the performance of the proposal. The results confirm the theoretical findings and show that the method is able to work with low-cost sensors.
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Parkinson, B.: Global positionig system: Theory and Applications. American Institute of Aeronautics and Astronautics, Reston (1996)
Zogg, J.: Essentials of satellite navigation, tech. rep., u-blox (2009)
Munguia, R., Urzua, S., Bolea, Y., Grau, A.: Vision-based slam system for unmanned aerial vehicles. Sensors 16(3), 372 (2016)
Artieda, J., Sebastian, J.M., Campoy, P., Correa, J., Mondragon, I.F., Martinez, C., Olivares, M.: Visual 3-d slam from uavs. J. Intell. Robot. Syst. 55, 299–321 (2009)
Weiss, S., Scaramuzza, D., Siegwart, R.: Monocular-slam based navigation for autonomous micro helicopters in gps-denied environments. J. Field Rob. 28(6), 854–874 (2011)
Davison, A., Reid, I., Molton, N., Stasse, O.: Monoslam: Real-time single camera slam. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1052–1067 (2007)
Mirzaei, F., Roumeliotis, S.: A kalman filter-based algorithm for imu-camera calibration: Observability analysis and performance evaluation. IEEE Trans. Robot. 24, 1143–1156 (2008)
Forster, C., Lynen, S., Kneip, L., Scaramuzza, D.: Collaborative monocular slam with multiple micro aerial vehicles. In: IROS, pp. 3962–3970. IEEE (2013)
Celik, K., Somani, A.K.: Monocular vision slam for indoor aerial vehicles. Journal of Electrical and Computer Engineering (2013)
Nutzi, G., Weiss, S., Scaramuzza, D., Siegwart, R.: Fusion of imu and vision for absolute scale estimation in monocular slam. J. Intell. Robot. Syst. 61(1-4), 287–299 (2011)
Wang, C.-L., Wang, T.-M., Liang, J.-H., Zhang, Y.-C., Zhou, Y.: Bearing-only visual slam for small unmanned aerial vehicles in gps-denied environments. Int. J. Autom. Comput. 10(5), 387–396 (2014)
Chowdhary, G., Johnson, E.N., Magree, D., Wu, A., Shein, A.: Gps-denied indoor and outdoor monocular vision aided navigation and control of unmanned aircraft. J. Field Rob. 30(3), 415–438 (2013)
Civera, J., Davison, A.J., Montiel, J.M.M.: ch. Dimensionless Monocular SLAM, pp. 412–419. Lecture Notes in Computer Science (2007)
Hermann, R., Krener, A.J.: Nonlinear controllability and observability, IEEE Transactions on Automatic Control (1977)
Slotine, J.E., Li, W.: Applied nonlinear control. Prentice-Hall Englewood Cliffs, New Jersey (1991)
Munguia, R., Grau, A.: A practical method for implementing an attitude and heading reference system. International Journal of Advanced Robotic Systems, vol. 11 (2014)
Euston, M., Coote, P., Mahony, R., Kim, J., Hamel, T.: A complementary filter for attitude estimation of a fixed-wing uav, in Intelligent Robots and Systems. In: IEEE/RSJ International Conference on 2008. IROS 2008, pp. 340–345 (2008)
Bouguet, J.: Camera calibration toolbox for matlab, in online (2008)
Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part i. IEEE Robot. Autom. Mag. 13, 99–110 (2006)
Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping (slam): part ii. IEEE Robot. Autom. Mag. 13, 108–117 (2006)
Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1508–1511 (2005)
Montiel, J.M.M., Civera, J., Davison, A.: Unified inverse depth parametrization for monocular slam. In: Proceedings of the Robotics Science and Systems Conference (2006)
Strasdat, H., Montiel, J., Davison, A.: Real-time monocular slam: Why filter? In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 2657–2664 (2010)
Civera, J., Davison, A., Montiel, J.: Inverse depth to depth conversion for monocular slam. In: 2007 IEEE International Conference on Robotics and Automation, pp. 2778–2783 (2007)
Corke, P.I.: Vision & robotics control: fundamental algorithms in matlab. Springer, Berlin (2011)
Community, O.S.: Ardupilot. http://ardupilot.com (2015)
Chatterjee, C., Roychowdhury, V.P.: Algorithms for coplanar camera calibration. Mach. Vis. Appl. 12, 84–97 (2000)
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This research was funded by Spanish Science ministry project “ColRobTrans MINECO DPI2016-78957-R AEI/FEDER EU”.
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Urzua, S., Munguía, R. & Grau, A. Monocular SLAM System for MAVs Aided with Altitude and Range Measurements: a GPS-free Approach. J Intell Robot Syst 94, 203–217 (2019). https://doi.org/10.1007/s10846-018-0775-y
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DOI: https://doi.org/10.1007/s10846-018-0775-y